from opencompass.openicl.icl_prompt_template import PromptTemplate
from opencompass.openicl.icl_retriever import ZeroRetriever
from opencompass.openicl.icl_inferencer import GenInferencer
from opencompass.datasets import LVEvalOPTRougeEvaluator, LVEvaldureaderDataset

LVEval_dureader_mixup_reader_cfg = dict(
    input_columns=["context", "input"],
    output_column="answers",
    train_split="test",
    test_split="test",
)

LVEval_dureader_mixup_infer_cfg = dict(
    prompt_template=dict(
        type=PromptTemplate,
        template=dict(
            round=[
                dict(
                    role="HUMAN",
                    prompt="请根据下面给定的文章回答问题，问题和答案只与其中一篇文章有关。\n\n文章：{context}\n\n现在请基于上述文章回答下面的问题，问题和答案只与其中一篇文章有关。\n\n问题：{input}\n回答：",
                ),
            ],
        ),
    ),
    retriever=dict(type=ZeroRetriever),
    inferencer=dict(type=GenInferencer, max_out_len=64),
)

LVEval_dureader_mixup_eval_cfg = dict(
    evaluator=dict(type=LVEvalOPTRougeEvaluator, language="zh"),
    pred_role="BOT",
)

DATASET_LENGTH_LEVEL = ["16k", "32k", "64k", "128k", "256k"]


def get_dataset_names(dataset_name, length_levels):
    datasets = []
    for length in length_levels:
        datasets.append(f"{dataset_name}_{length}")
    return datasets


LVEval_dureader_mixup_datasets = [
    dict(
        type=LVEvaldureaderDataset,
        abbr="LVEval_" + name_len,
        path="Infinigence/LVEval",
        name=name_len,
        reader_cfg=LVEval_dureader_mixup_reader_cfg,
        infer_cfg=LVEval_dureader_mixup_infer_cfg,
        eval_cfg=LVEval_dureader_mixup_eval_cfg,
    )
    for name_len in get_dataset_names("dureader_mixup", DATASET_LENGTH_LEVEL)
]
